Utilizing Large language models to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation

Cai, Xiangming, Geng, Yuanming, Du, Yiming, Westerman, Bart, Wang, Duolao ORCID: https://orcid.org/0000-0003-2788-2464, Ma, Chiyuan and Vallejo, Juan J. Garcia (2025) 'Utilizing Large language models to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation'. BMC Medical Research Methodology, Vol 25, Issue 1, e116.

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Abstract

Background: Large language models (LLMs) like ChatGPT showed great potential in aiding medical research. A heavy workload in filtering records is needed during the research process of evidence-based medicine, especially meta-analysis. However, few studies tried to use LLMs to help screen records in meta-analysis.

Objective: In this research, we aimed to explore the possibility of incorporating multiple LLMs to facilitate the screening step based on the title and abstract of records during meta-analysis.

Methods: Various LLMs were evaluated, which includes GPT-3.5, GPT-4, Deepseek-R1-Distill, Qwen-2.5, Phi-4, Llama-3.1, Gemma-2 and Claude-2. To assess our strategy, we selected three meta-analyses from the literature, together with a glioma meta-analysis embedded in the study, as additional validation. For the automatic selection of records from curated meta-analyses, a four-step strategy called LARS-GPT was developed, consisting of (1) criteria selection and single-prompt (prompt with one criterion) creation, (2) best combination identification, (3) combined-prompt (prompt with one or more criteria) creation, and (4) request sending and answer summary. Recall, workload reduction, precision, and F1 score were calculated to assess the performance of LARS-GPT.

Results: A variable performance was found between different single-prompts, with a mean recall of 0.800. Based on these single-prompts, we were able to find combinations with better performance than the pre-set threshold. Finally, with a best combination of criteria identified, LARS-GPT showed a 40.1% workload reduction on average with a recall greater than 0.9.

Conclusions: We show here the groundbreaking finding that automatic selection of literature for meta-analysis is possible with LLMs. We provide it here as a pipeline, LARS-GPT, which showed a great workload reduction while maintaining a pre-set recall.

Item Type:	Article
Subjects:	W General Medicine. Health Professions > W 82 Biomedical technology (General) W General Medicine. Health Professions > W 26.5 Informatics. Health informatics W General Medicine. Health Professions > W 20.5 Biomedical research
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1186/s12874-025-02569-3
SWORD Depositor:	JISC Pubrouter
Depositing User:	JISC Pubrouter
Date Deposited:	07 May 2025 14:23
Last Modified:	07 May 2025 14:23
URI:	https://archive.lstmed.ac.uk/id/eprint/26640

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